When the environment consists of a large number of states and actions, it will be very expensive to compute the Q value of all possible state-action pairs in an exhaustive fashion. So, instead of computing Q values in this way, can we approximate them using any function approximator, such as a neural network.
In a Deep Q-Network we can parameterize our Q function by a parameter 𝜃 and compute the Q value where the parameter 𝜃 is just the parameter of our neural network. So, we just feed the state of the environment to a neural network and it will return the Q value of all possible actions in that state.
Once we obtain the Q values, then we can select the best action as the one that has the maximum Q value.
